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Mapping Inequalities of Access to Employment and Quantifying Transport Poverty in Canadian Cities

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Mapping Inequalities of Access to Employment and Quantifying Transport Poverty in Canadian Cities Mapping Inequalities of Access to Employment and Quantifying Transport Poverty in Canadian Cities by Jeff Allen A thesis submitted in conformity with the requirements for the degree of Master of Arts Department of Geography and Planning University of Toronto c Copyright 2018 by Jeff Allen Abstract Mapping Inequalities of Access to Employment and Quantifying Transport Poverty in Canadian Cities Jeff Allen Master of Arts Department of Geography and Planning University of Toronto 2018 Millions of Canadians rely on public transportation to conduct daily activities and participate in the labour force. However, many are disadvantaged because exist- ing public transit service does not provide sufficient access to destinations. Limited transit options, compounded with socioeconomic factors like not having a private ve- hicle, can result in transport poverty, limiting travel to important destinations, like employment opportunities. Accordingly, the objective of this thesis is to develop ac- curate measures of accessibility to examine the degree to which the Canadian urban population can reach employment opportunities via public transit. These measures are used to analyze inequalities in accessibility with respect to socioeconomic status and estimate where, and to what extent, Canadians are at risk of transport poverty. This knowledge is able to inform policy aimed to increase transit ridership, reduce inequalities of transit accessibility, and limit transport-related barriers to activity participation. ii Contents Table of Contents . iv List of Figures . v List of Tables . viii List of Acronyms & Abbreviations . ix 1 Introduction 1 2 Background 4 2.1 Urban Transportation & Accessibility . 4 2.2 Measuring Accessibility . 6 2.3 Inequalities of Urban Transportation . 10 2.4 Access to Employment . 14 2.5 Canadian Context . 17 3 Study Areas & Data 21 3.1 Study Areas . 21 3.2 Demographic & Employment Data . 23 3.3 Network Graphs . 26 4 Measuring Access to Employment 27 4.1 Computing Travel Times . 27 4.2 Cumulative Accessibility . 28 4.3 Gravity Accessibility . 30 4.4 Competitive Accessibility . 31 4.5 Validating Results . 44 4.6 Disseminating & Visualizing Results . 45 iii 5 Inequalities of Transit Access to Employment 47 5.1 Overall Regional & Modal Comparisons . 47 5.2 Horizontal Equity . 50 5.3 Transit Access & Income Inequalities . 53 5.4 Estimating the Extent of Transport Poverty . 57 5.5 Characteristics of Areas Vulnerable to Transport Poverty . 64 6 Policy Recommendations 74 7 Conclusion 80 References 83 A Appendix of Maps 95 A.1 Choropleths of transit access to employment and low income households 95 A.2 Maps of the cluster analysis of areas at high risk of transport poverty 105 iv List of Figures 1 Location of study areas . 21 2 Example of the temporal differences in commute time by transit from a residential neighbourhood to a mall in northwestern Toronto . 27 3 Differences in cumulative accessibility by departure time for a dis- semination area (dauid = 46110663) in Winnipeg . 30 4 Gravity functions . 31 5 Example city for measuring competitive accessibility . 34 6 Convergence of Ai for the scenario presented in Figure 5 . 35 7 Example scenario where there are more jobs than workers . 37 8 Convergence of the scenario presented in Figure 7 . 37 9 Example of where the labour force is greater than the number of jobs 38 10 Convergence of the scenario presented in Figure 9 . 39 11 A comparison of two cities . 39 12 Example city for measuring competitive accessibility . 42 13 Convergence of the example in Figure 12 with two travel modes (red is by transit, white is by car) . 43 14 Plot indicating the mean and distribution of access to jobs by transit 48 15 Plot indicating the mean and distribution of access to jobs by car . 49 16 Lorenz Curve for access to jobs by transit and auto . 53 17 Scatter plot of transit access to jobs and the combined measure of socio-economic status (Iµ)........................ 57 18 Classifying DAs in terms of risk of experiencing transport poverty . 65 19 Plot of population density in DAs classified by risk of transport poverty 67 20 Plot of dwelling type in areas with low transit access and areas at risk of transport poverty . 68 v 21 Plot of dwelling period of construction in DAs classified by risk of transport poverty . 69 22 Plot of mobility status in DAs classified by risk of transport poverty 70 23 Scree plot for determining the number of clusters, k . 72 24 Map of the location of cluster groups for Vancouver . 73 25 Map of Quebec City showing transit access to employment and low income households . 95 26 Map of Montreal showing transit access to employment and low in- come households . 96 27 Map of Ottawa showing transit access to employment and low income households . 97 28 Map of the Toronto region showingtransit access to employment and low income households . 98 29 Map of Toronto centre showing transit access to employment and low income households . 99 30 Map of Hamilton, Guelph, and Kitchener-Waterloo showing transit access to employment and low income households . 100 31 Map of Winnipeg showing transit access to employment and low in- come households . 101 32 Map of Calgary showing transit access to employment and low income households . 102 33 Map of Edmonton showing transit access to employment and low income households . 103 34 Maps of Vancouver showing transit access to employment and low income households . 104 35 Map of the location of cluster groups for Quebec City . 105 vi 36 Map of the location of cluster groups for Montreal . 106 37 Map of the location of cluster groups for Ottawa . 107 38 Map of the location of cluster groups for Toronto . 108 39 Map of the location of cluster groups for Winnipeg . 109 40 Map of the location of cluster groups for Calgary . 110 41 Map of the location of cluster groups for Edmonton . 111 42 Map of the location of cluster groups for Vancouver . 112 vii List of Tables 1 Summary statistics by urban region . 22 2 Summary of CMAs and Transit Agencies used in this study . 24 3 Correlation coefficients between transit access to jobs and transit mode share for journey to work trips . 45 4 Summary statistics of access to jobs by mode and urban region . 48 5 CV and Gini coefficients of access to employment by mode . 51 6 Correlation coefficient between transit access to jobs and income- related variables . 54 7 Correlation coefficient between income-related variables and transit access to low income jobs (under $20,000 per year) . 55 8 Correlation coefficient between income-related variables and transit access to low-to-medium income jobs ($20,000 - $40,000) . 56 9 Counts of all low-income residents, unemployed, and recent immi- grants (2011-2016) in the lowest decile and lowest quintile of transit access by region . 60 10 Counts of all low-income residents, unemployed, and recent immi- grants (2011-2016) in areas of low (<0.1) and extremely low (<0.05) transit access . 61 11 Count of all low-income residents, unemployed, and recent immi- grants (2011-2016) in areas where the ratio of transit access to auto access is less than 0.2 and 0.1 . 63 12 Percent of DAs classified by risk of experiencing transport poverty in each region . 66 13 Cluster analysis results of DAs at risk of transport poverty . 71 viii List of Acronyms & Abbreviations BRT Bus Rapid Transit DA Dissemination Area CT Census Tract CV Coefficient of Variation GIS Geographic Information Systems GPS Global Positioning System GTA Greater Toronto Area GTFS General Transit Feed Specification LRT Light Rail Transit OSM OpenStreetMap OSRM Open Source Routing Machine OTP OpenTripPlanner Routing Engine SES Socioeconomic Status ix 1 Introduction Public transit is paramount in providing many urban Canadians with the ability to travel to daily activities and participate in the labour force. Especially within lower income groups, transit is often the only means for accomplishing independent travel in Canada's expanding metropolises. Despite this reliance, many neighbourhoods are disadvantaged because public transit does not provide sufficient access to des- tinations, like employment opportunities. Poor transit access, combined with other forms of social and economic disadvantage (e.g. poor health, not being able to af- ford a car, etc.), can result in transport poverty (Casas, 2007; Preston & Raj´e,2007; K. Lucas, 2012). This can limit people in their ability to find employment oppor- tunities and participate in the labour force. The Canadian government is currently investing billions of dollars in public transport, and social equity and inclusion are part of policy goals across the country (Government of Canada, 2017). However, the extent of inequalities in transit accessibility, and the number of people at risk of transport poverty, are unknown at a national scale. Accordingly, the contributions of this thesis are as follows: 1) Develop a novel methodology for computing comparative measures of access to employment for Canada's eight largest cities. 2) Analyze the inequalities of transit access to employ- ment in Canada with respect to socioeconomic status. 3) Estimate where, and to what extent, Canadians are at risk of transport poverty. 4) Describe and generate typologies for areas vulnerable to transport poverty in order to recommend urban planning strategies which reduce inequalities and limit the risks of transport-related exclusion. In generating a comparative measure of access to employment, we account for minute-by-minute variations in transit schedules, competition both among the labour force for jobs as well as employers for employees, and appropriately stan- 1 dardize in order to compare between regions which have different levels of imbal- ance between the number of jobs and the size of the labour force. We use this formulation to compute access to jobs for Canada's eight largest cities (Toronto, Montreal, Quebec City, Ottawa, Winnipeg, Calgary, Edmonton, and Vancouver).
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